Overview

Dataset statistics

Number of variables23
Number of observations3660
Missing cells6621
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory686.2 KiB
Average record size in memory192.0 B

Variable types

Categorical13
Numeric10

Alerts

society has a high cardinality: 674 distinct valuesHigh cardinality
sector has a high cardinality: 113 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2349 distinct valuesHigh cardinality
price is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price and 7 other fieldsHigh correlation
built_up_area is highly overall correlated with price and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 2 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
store room is highly imbalanced (55.8%)Imbalance
others is highly imbalanced (50.1%)Imbalance
facing has 1039 (28.4%) missing valuesMissing
super_built_up_area has 1785 (48.8%) missing valuesMissing
built_up_area has 1986 (54.3%) missing valuesMissing
carpet_area has 1791 (48.9%) missing valuesMissing
area is highly skewed (γ1 = 29.73095613)Skewed
built_up_area is highly skewed (γ1 = 40.52309524)Skewed
carpet_area is highly skewed (γ1 = 24.31382719)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 459 (12.5%) zerosZeros

Reproduction

Analysis started2023-12-05 15:45:48.459712
Analysis finished2023-12-05 15:46:01.214910
Duration12.76 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
flat
2817 
house
843 

Length

Max length5
Median length4
Mean length4.2303279
Min length4

Characters and Unicode

Total characters15483
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2817
77.0%
house 843
 
23.0%

Length

2023-12-05T21:16:01.287435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:01.386262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2817
77.0%
house 843
 
23.0%

Most occurring characters

ValueCountFrequency (%)
f 2817
18.2%
l 2817
18.2%
a 2817
18.2%
t 2817
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15483
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2817
18.2%
l 2817
18.2%
a 2817
18.2%
t 2817
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 15483
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2817
18.2%
l 2817
18.2%
a 2817
18.2%
t 2817
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2817
18.2%
l 2817
18.2%
a 2817
18.2%
t 2817
18.2%
h 843
 
5.4%
o 843
 
5.4%
u 843
 
5.4%
s 843
 
5.4%
e 843
 
5.4%

society
Categorical

Distinct674
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size57.2 KiB
independent
481 
tulip violet
 
75
ss the leaf
 
73
dlf new town heights
 
42
shapoorji pallonji joyville gurugram
 
42
Other values (669)
2946 

Length

Max length49
Median length39
Mean length16.86909
Min length1

Characters and Unicode

Total characters61724
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)8.4%

Sample

1st rowshree vardhman victoria
2nd rowsupertech araville
3rd rowss the leaf
4th rowdlf new town heights
5th rowchd avenue

Common Values

ValueCountFrequency (%)
independent 481
 
13.1%
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
dlf new town heights 42
 
1.1%
shapoorji pallonji joyville gurugram 42
 
1.1%
signature global park 35
 
1.0%
shree vardhman victoria 34
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
smart world orchard 32
 
0.9%
dlf the ultima 31
 
0.8%
Other values (664) 2782
76.0%

Length

2023-12-05T21:16:01.482805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 486
 
5.0%
the 350
 
3.6%
dlf 219
 
2.3%
park 209
 
2.2%
city 163
 
1.7%
m3m 152
 
1.6%
global 152
 
1.6%
emaar 151
 
1.6%
signature 149
 
1.5%
heights 134
 
1.4%
Other values (783) 7470
77.5%

Most occurring characters

ValueCountFrequency (%)
e 6672
 
10.8%
5978
 
9.7%
a 5836
 
9.5%
r 4153
 
6.7%
n 4138
 
6.7%
i 3814
 
6.2%
t 3702
 
6.0%
s 3464
 
5.6%
l 2927
 
4.7%
o 2744
 
4.4%
Other values (31) 18296
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55203
89.4%
Space Separator 5978
 
9.7%
Decimal Number 525
 
0.9%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6672
12.1%
a 5836
 
10.6%
r 4153
 
7.5%
n 4138
 
7.5%
i 3814
 
6.9%
t 3702
 
6.7%
s 3464
 
6.3%
l 2927
 
5.3%
o 2744
 
5.0%
d 2473
 
4.5%
Other values (16) 15280
27.7%
Decimal Number
ValueCountFrequency (%)
3 206
39.2%
2 81
 
15.4%
1 75
 
14.3%
6 56
 
10.7%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
5978
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55203
89.4%
Common 6521
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6672
12.1%
a 5836
 
10.6%
r 4153
 
7.5%
n 4138
 
7.5%
i 3814
 
6.9%
t 3702
 
6.7%
s 3464
 
6.3%
l 2927
 
5.3%
o 2744
 
5.0%
d 2473
 
4.5%
Other values (16) 15280
27.7%
Common
ValueCountFrequency (%)
5978
91.7%
3 206
 
3.2%
2 81
 
1.2%
1 75
 
1.2%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6672
 
10.8%
5978
 
9.7%
a 5836
 
9.5%
r 4153
 
6.7%
n 4138
 
6.7%
i 3814
 
6.2%
t 3702
 
6.0%
s 3464
 
5.6%
l 2927
 
4.7%
o 2744
 
4.4%
Other values (31) 18296
29.6%

sector
Categorical

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
sohna road
 
154
sector 85
 
108
sector 102
 
107
sector 92
 
100
sector 69
 
93
Other values (108)
3098 

Length

Max length26
Median length9
Mean length9.3224044
Min length7

Characters and Unicode

Total characters34120
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 70
2nd rowsector 79
3rd rowsector 85
4th rowsector 90
5th rowsector 71

Common Values

ValueCountFrequency (%)
sohna road 154
 
4.2%
sector 85 108
 
3.0%
sector 102 107
 
2.9%
sector 92 100
 
2.7%
sector 69 93
 
2.5%
sector 90 89
 
2.4%
sector 81 87
 
2.4%
sector 65 87
 
2.4%
sector 109 86
 
2.3%
sector 79 76
 
2.1%
Other values (103) 2673
73.0%

Length

2023-12-05T21:16:01.590317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3435
46.7%
road 178
 
2.4%
sohna 166
 
2.3%
85 108
 
1.5%
102 107
 
1.5%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (106) 2898
39.4%

Most occurring characters

ValueCountFrequency (%)
o 3790
11.1%
3688
10.8%
s 3680
10.8%
r 3680
10.8%
e 3525
10.3%
c 3486
10.2%
t 3446
10.1%
1 1071
 
3.1%
0 802
 
2.4%
8 778
 
2.3%
Other values (21) 6174
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23197
68.0%
Decimal Number 7235
 
21.2%
Space Separator 3688
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3790
16.3%
s 3680
15.9%
r 3680
15.9%
e 3525
15.2%
c 3486
15.0%
t 3446
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
1.0%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1071
14.8%
0 802
11.1%
8 778
10.8%
9 763
10.5%
6 734
10.1%
7 684
9.5%
2 672
9.3%
3 661
9.1%
5 589
8.1%
4 481
6.6%
Space Separator
ValueCountFrequency (%)
3688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23197
68.0%
Common 10923
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3790
16.3%
s 3680
15.9%
r 3680
15.9%
e 3525
15.2%
c 3486
15.0%
t 3446
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
1.0%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
ValueCountFrequency (%)
3688
33.8%
1 1071
 
9.8%
0 802
 
7.3%
8 778
 
7.1%
9 763
 
7.0%
6 734
 
6.7%
7 684
 
6.3%
2 672
 
6.2%
3 661
 
6.1%
5 589
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3790
11.1%
3688
10.8%
s 3680
10.8%
r 3680
10.8%
e 3525
10.3%
c 3486
10.2%
t 3446
10.1%
1 1071
 
3.1%
0 802
 
2.4%
8 778
 
2.3%
Other values (21) 6174
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:01.697861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2023-12-05T21:16:01.804428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.6%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:01.921855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2023-12-05T21:16:02.030370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3509
95.9%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:02.147008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2023-12-05T21:16:02.256593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
89.3%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%

areaWithType
Categorical

Distinct2349
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
Plot area 360(301.01 sq.m.)
 
36
Plot area 300(250.84 sq.m.)
 
26
Plot area 200(167.23 sq.m.)
 
19
Plot area 502(419.74 sq.m.)
 
18
Super Built up area 1578(146.6 sq.m.)
 
17
Other values (2344)
3544 

Length

Max length124
Median length119
Mean length54.300273
Min length12

Characters and Unicode

Total characters198739
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1846 ?
Unique (%)50.4%

Sample

1st rowSuper Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)
2nd rowSuper Built up area 1295(120.31 sq.m.)Carpet area: 1250 sq.ft. (116.13 sq.m.)
3rd rowSuper Built up area 1772(164.62 sq.m.)Built Up area: 1300 sq.ft. (120.77 sq.m.)Carpet area: 916 sq.ft. (85.1 sq.m.)
4th rowCarpet area: 1447 (134.43 sq.m.)
5th rowSuper Built up area 1198(111.3 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 36
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Plot area 502(419.74 sq.m.) 18
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Plot area 270(225.75 sq.m.) 16
 
0.4%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Plot area 150(125.42 sq.m.) 14
 
0.4%
Super Built up area 2010(186.74 sq.m.) 14
 
0.4%
Other values (2339) 3468
94.8%

Length

2023-12-05T21:16:02.377753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5551
18.5%
sq.m 3638
12.1%
up 3015
 
10.0%
built 2314
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1183
 
3.9%
sq.m.)built 699
 
2.3%
carpet 682
 
2.3%
plot 667
 
2.2%
Other values (2840) 8665
28.8%

Most occurring characters

ValueCountFrequency (%)
26380
 
13.3%
. 20317
 
10.2%
a 13102
 
6.6%
r 9426
 
4.7%
e 9295
 
4.7%
1 9193
 
4.6%
s 7535
 
3.8%
q 7404
 
3.7%
t 7302
 
3.7%
u 6765
 
3.4%
Other values (25) 82020
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82487
41.5%
Decimal Number 46954
23.6%
Space Separator 26380
 
13.3%
Other Punctuation 23326
 
11.7%
Uppercase Letter 8566
 
4.3%
Close Punctuation 5513
 
2.8%
Open Punctuation 5513
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13102
15.9%
r 9426
11.4%
e 9295
11.3%
s 7535
9.1%
q 7404
9.0%
t 7302
8.9%
u 6765
8.2%
p 6759
8.2%
m 5522
6.7%
l 3682
 
4.5%
Other values (5) 5695
6.9%
Decimal Number
ValueCountFrequency (%)
1 9193
19.6%
0 6590
14.0%
2 5661
12.1%
5 4692
10.0%
3 3944
8.4%
4 3689
7.9%
6 3659
 
7.8%
7 3240
 
6.9%
8 3152
 
6.7%
9 3134
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3015
35.2%
S 1875
21.9%
C 1869
21.8%
U 1140
 
13.3%
P 667
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 20317
87.1%
: 3009
 
12.9%
Space Separator
ValueCountFrequency (%)
26380
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5513
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107686
54.2%
Latin 91053
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13102
14.4%
r 9426
10.4%
e 9295
10.2%
s 7535
8.3%
q 7404
8.1%
t 7302
8.0%
u 6765
7.4%
p 6759
7.4%
m 5522
 
6.1%
l 3682
 
4.0%
Other values (10) 14261
15.7%
Common
ValueCountFrequency (%)
26380
24.5%
. 20317
18.9%
1 9193
 
8.5%
0 6590
 
6.1%
2 5661
 
5.3%
) 5513
 
5.1%
( 5513
 
5.1%
5 4692
 
4.4%
3 3944
 
3.7%
4 3689
 
3.4%
Other values (5) 16194
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26380
 
13.3%
. 20317
 
10.2%
a 13102
 
6.6%
r 9426
 
4.7%
e 9295
 
4.7%
1 9193
 
4.6%
s 7535
 
3.8%
q 7404
 
3.7%
t 7302
 
3.7%
u 6765
 
3.4%
Other values (25) 82020
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3480874
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:02.476368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8800121
Coefficient of variation (CV)0.56151823
Kurtosis18.504495
Mean3.3480874
Median Absolute Deviation (MAD)1
Skewness3.5004336
Sum12254
Variance3.5344456
MonotonicityNot monotonic
2023-12-05T21:16:02.679343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.9%
2 940
25.7%
4 659
18.0%
5 200
 
5.5%
1 124
 
3.4%
6 73
 
2.0%
9 40
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 27
 
0.7%
Other values (9) 43
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 940
25.7%
3 1496
40.9%
4 659
18.0%
5 200
 
5.5%
6 73
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 40
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 11
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 27
0.7%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4131148
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:02.770934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9291474
Coefficient of variation (CV)0.56521611
Kurtosis17.899933
Mean3.4131148
Median Absolute Deviation (MAD)1
Skewness3.2651882
Sum12492
Variance3.7216099
MonotonicityNot monotonic
2023-12-05T21:16:02.854965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1076
29.4%
2 1045
28.6%
4 818
22.3%
5 289
 
7.9%
1 155
 
4.2%
6 117
 
3.2%
9 40
 
1.1%
7 38
 
1.0%
8 24
 
0.7%
12 21
 
0.6%
Other values (9) 37
 
1.0%
ValueCountFrequency (%)
1 155
 
4.2%
2 1045
28.6%
3 1076
29.4%
4 818
22.3%
5 289
 
7.9%
6 117
 
3.2%
7 38
 
1.0%
8 24
 
0.7%
9 40
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 7
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 21
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
3+
1161 
3
1072 
2
882 
1
364 
0
181 

Length

Max length2
Median length1
Mean length1.3172131
Min length1

Characters and Unicode

Total characters4821
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row3
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 1161
31.7%
3 1072
29.3%
2 882
24.1%
1 364
 
9.9%
0 181
 
4.9%

Length

2023-12-05T21:16:02.948572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:03.041191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2233
61.0%
2 882
 
24.1%
1 364
 
9.9%
0 181
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2233
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.6%
0 181
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
75.9%
Math Symbol 1161
 
24.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2233
61.0%
2 882
 
24.1%
1 364
 
9.9%
0 181
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2233
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.6%
0 181
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2233
46.3%
+ 1161
24.1%
2 882
 
18.3%
1 364
 
7.6%
0 181
 
3.8%

floorNum
Real number (ℝ)

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8151607
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:03.138685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0195333
Coefficient of variation (CV)0.88325626
Kurtosis4.4946742
Mean6.8151607
Median Absolute Deviation (MAD)3
Skewness1.6887343
Sum24814
Variance36.234781
MonotonicityNot monotonic
2023-12-05T21:16:03.246554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 493
13.5%
2 488
13.3%
1 349
 
9.5%
4 312
 
8.5%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 936
25.6%
ValueCountFrequency (%)
0 129
 
3.5%
1 349
9.5%
2 488
13.3%
3 493
13.5%
4 312
8.5%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1039
Missing (%)28.4%
Memory size57.2 KiB
North-East
621 
East
620 
North
386 
West
247 
South
231 
Other values (3)
516 

Length

Max length10
Median length5
Mean length6.8382297
Min length4

Characters and Unicode

Total characters17923
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth-East
2nd rowNorth
3rd rowNorth-East
4th rowNorth-West
5th rowSouth

Common Values

ValueCountFrequency (%)
North-East 621
17.0%
East 620
16.9%
North 386
 
10.5%
West 247
 
6.7%
South 231
 
6.3%
North-West 192
 
5.2%
South-East 171
 
4.7%
South-West 153
 
4.2%
(Missing) 1039
28.4%

Length

2023-12-05T21:16:03.345139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:03.447791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
north-east 621
23.7%
east 620
23.7%
north 386
14.7%
west 247
 
9.4%
south 231
 
8.8%
north-west 192
 
7.3%
south-east 171
 
6.5%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3758
21.0%
s 2004
11.2%
o 1754
9.8%
h 1754
9.8%
E 1412
 
7.9%
a 1412
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13028
72.7%
Uppercase Letter 3758
 
21.0%
Dash Punctuation 1137
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3758
28.8%
s 2004
15.4%
o 1754
13.5%
h 1754
13.5%
a 1412
 
10.8%
r 1199
 
9.2%
e 592
 
4.5%
u 555
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1412
37.6%
N 1199
31.9%
W 592
15.8%
S 555
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16786
93.7%
Common 1137
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3758
22.4%
s 2004
11.9%
o 1754
10.4%
h 1754
10.4%
E 1412
 
8.4%
a 1412
 
8.4%
N 1199
 
7.1%
r 1199
 
7.1%
W 592
 
3.5%
e 592
 
3.5%
Other values (2) 1110
 
6.6%
Common
ValueCountFrequency (%)
- 1137
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3758
21.0%
s 2004
11.2%
o 1754
9.8%
h 1754
9.8%
E 1412
 
7.9%
a 1412
 
7.9%
N 1199
 
6.7%
r 1199
 
6.7%
- 1137
 
6.3%
W 592
 
3.3%
Other values (3) 1702
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
Relatively New
1640 
New Property
590 
Moderately Old
558 
Undefined
445 
Old Property
302 

Length

Max length18
Median length14
Mean length13.041257
Min length9

Characters and Unicode

Total characters47731
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowNew Property
3rd rowRelatively New
4th rowOld Property
5th rowModerately Old

Common Values

ValueCountFrequency (%)
Relatively New 1640
44.8%
New Property 590
 
16.1%
Moderately Old 558
 
15.2%
Undefined 445
 
12.2%
Old Property 302
 
8.3%
Under Construction 125
 
3.4%

Length

2023-12-05T21:16:03.545404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:03.640993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2230
32.4%
relatively 1640
23.9%
property 892
 
13.0%
old 860
 
12.5%
moderately 558
 
8.1%
undefined 445
 
6.5%
under 125
 
1.8%
construction 125
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 8533
17.9%
l 4698
 
9.8%
t 3340
 
7.0%
3215
 
6.7%
y 3090
 
6.5%
r 2592
 
5.4%
d 2433
 
5.1%
N 2230
 
4.7%
w 2230
 
4.7%
i 2210
 
4.6%
Other values (15) 13160
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37641
78.9%
Uppercase Letter 6875
 
14.4%
Space Separator 3215
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8533
22.7%
l 4698
12.5%
t 3340
 
8.9%
y 3090
 
8.2%
r 2592
 
6.9%
d 2433
 
6.5%
w 2230
 
5.9%
i 2210
 
5.9%
a 2198
 
5.8%
o 1700
 
4.5%
Other values (7) 4617
12.3%
Uppercase Letter
ValueCountFrequency (%)
N 2230
32.4%
R 1640
23.9%
P 892
 
13.0%
O 860
 
12.5%
U 570
 
8.3%
M 558
 
8.1%
C 125
 
1.8%
Space Separator
ValueCountFrequency (%)
3215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44516
93.3%
Common 3215
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8533
19.2%
l 4698
10.6%
t 3340
 
7.5%
y 3090
 
6.9%
r 2592
 
5.8%
d 2433
 
5.5%
N 2230
 
5.0%
w 2230
 
5.0%
i 2210
 
5.0%
a 2198
 
4.9%
Other values (14) 10962
24.6%
Common
ValueCountFrequency (%)
3215
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8533
17.9%
l 4698
 
9.8%
t 3340
 
7.0%
3215
 
6.7%
y 3090
 
6.5%
r 2592
 
5.4%
d 2433
 
5.1%
N 2230
 
4.7%
w 2230
 
4.7%
i 2210
 
4.6%
Other values (15) 13160
27.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1785
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:03.755621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2023-12-05T21:16:03.868781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.6%
(Missing) 1785
48.8%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct641
Distinct (%)38.3%
Missing1986
Missing (%)54.3%
Infinite0
Infinite (%)0.0%
Mean2386.1989
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:03.983394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.65
Q11110.5
median1650
Q32400
95-th percentile4660.5
Maximum737147
Range737145
Interquartile range (IQR)1289.5

Descriptive statistics

Standard deviation18026.927
Coefficient of variation (CV)7.5546621
Kurtosis1652.6076
Mean2386.1989
Median Absolute Deviation (MAD)619.5
Skewness40.523095
Sum3994497
Variance3.2497009 × 108
MonotonicityNot monotonic
2023-12-05T21:16:04.096012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 32
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (631) 1372
37.5%
(Missing) 1986
54.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct731
Distinct (%)39.1%
Missing1791
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2532.5853
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:04.210629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22817.978
Coefficient of variation (CV)9.009757
Kurtosis603.569
Mean2532.5853
Median Absolute Deviation (MAD)470
Skewness24.313827
Sum4733401.9
Variance5.2066012 × 108
MonotonicityNot monotonic
2023-12-05T21:16:04.320177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (721) 1575
43.0%
(Missing) 1791
48.9%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
2963 
1
697 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

Length

2023-12-05T21:16:04.419375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:04.504023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2963
81.0%
1 697
 
19.0%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
2341 
1
1319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

Length

2023-12-05T21:16:04.577395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:04.663455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

Most occurring characters

ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2341
64.0%
1 1319
36.0%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
3325 
1
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

Length

2023-12-05T21:16:04.736028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:04.935613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3325
90.8%
1 335
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
3010 
1
650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

Length

2023-12-05T21:16:05.009230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:05.093848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3010
82.2%
1 650
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
3258 
1
402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

Length

2023-12-05T21:16:05.167850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:05.251463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3258
89.0%
1 402
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
0
2424 
1
1035 
2
 
201

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3660
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

Length

2023-12-05T21:16:05.324203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T21:16:05.412496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2424
66.2%
1 1035
28.3%
2 201
 
5.5%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.535519
Minimum0
Maximum174
Zeros459
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-12-05T21:16:05.502108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.067275
Coefficient of variation (CV)0.74183113
Kurtosis-0.87960822
Mean71.535519
Median Absolute Deviation (MAD)38
Skewness0.45973511
Sum261820
Variance2816.1357
MonotonicityNot monotonic
2023-12-05T21:16:05.608722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459
 
12.5%
49 347
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
42 45
 
1.2%
37 45
 
1.2%
Other values (151) 2300
62.8%
ValueCountFrequency (%)
0 459
12.5%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 42
 
1.1%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 27
 
0.7%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2023-12-05T21:15:59.430747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.205083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.264771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.347992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.280694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.287691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.394736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.330234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.397592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.441678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.530229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.396039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.362321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.438531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.379284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.498819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.487254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.420852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.496108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.536211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.633778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.492619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.463840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.532267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.479910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.600335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.581903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.518363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.598729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.636793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.727298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.582516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.647457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.617782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.574436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.695957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.670130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.607033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.690347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.740377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.839927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.685029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.756033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.714418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.679943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.802585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.768877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.707027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.819262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.844942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.945405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.790547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.858670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.812941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.792345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.907820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.870454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.802577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.944757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.947284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:16:00.048935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.883062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.951680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.903489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.887554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.999398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.957687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.891947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.046312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.038444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:16:00.266514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:50.973615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.046193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.993978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.982076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.090575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.046233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.988200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.136367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.137029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:16:00.371027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.070740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.149951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.089493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.086591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.194135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.143781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.086777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.241851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.229575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:16:00.467418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:51.167304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:52.248440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:53.188075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:54.185145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:55.292219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:56.236360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:57.301972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:58.334430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-05T21:15:59.331089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-12-05T21:16:05.720234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_arealuxury_scoreproperty_typebalconyfacingagePossessionstudy roomservant roomstore roompooja roomothersfurnishing_type
price1.0000.7440.7440.6810.7200.0010.7720.6050.6130.2150.5430.1360.0210.1020.2440.3690.3030.3340.0340.175
price_per_sqft0.7441.0000.2070.4170.411-0.1260.2870.1320.1360.0540.2010.0330.0000.0460.0300.0440.0000.0430.0360.022
area0.7440.2071.0000.6240.6870.1160.9480.8350.8010.2590.0280.0110.0220.0000.0180.0150.0390.0370.0420.043
bedRoom0.6810.4170.6241.0000.862-0.1000.8000.3970.5730.0580.5920.1740.0290.1240.1480.3150.2240.2880.0730.166
bathroom0.7200.4110.6870.8621.000-0.0020.8190.4860.6030.1800.4690.2240.0390.1070.1700.5190.2440.2840.0640.192
floorNum0.001-0.1260.116-0.100-0.0021.0000.1520.0900.1580.2320.4820.0790.0000.1230.0760.0860.1110.1010.0330.026
super_built_up_area0.7720.2870.9480.8000.8190.1521.0000.9260.8940.2221.0000.3060.0000.0800.1210.5840.0460.1570.0840.132
built_up_area0.6050.1320.8350.3970.4860.0900.9261.0000.9690.2940.0000.0001.0000.0000.0000.0000.0000.0000.0000.091
carpet_area0.6130.1360.8010.5730.6030.1580.8940.9691.0000.2400.0000.0260.0000.0000.0040.0000.0000.0000.0170.000
luxury_score0.2150.0540.2590.0580.1800.2320.2220.2940.2401.0000.3300.2240.0650.2240.1800.3460.2290.1870.1750.238
property_type0.5430.2010.0280.5920.4690.4821.0000.0000.0000.3301.0000.2120.0930.3560.1230.0620.2410.2500.0240.085
balcony0.1360.0330.0110.1740.2240.0790.3060.0000.0260.2240.2121.0000.0170.2290.1800.4390.1440.1940.0800.177
facing0.0210.0000.0220.0290.0390.0000.0001.0000.0000.0650.0930.0171.0000.0900.0000.0340.0370.0310.0000.054
agePossession0.1020.0460.0000.1240.1070.1230.0800.0000.0000.2240.3560.2290.0901.0000.1090.2820.1420.1860.1070.214
study room0.2440.0300.0180.1480.1700.0760.1210.0000.0040.1800.1230.1800.0000.1091.0000.1810.2260.3090.0260.136
servant room0.3690.0440.0150.3150.5190.0860.5840.0000.0000.3460.0620.4390.0340.2820.1811.0000.1590.2490.0000.265
store room0.3030.0000.0390.2240.2440.1110.0460.0000.0000.2290.2410.1440.0370.1420.2260.1591.0000.3050.1060.154
pooja room0.3340.0430.0370.2880.2840.1010.1570.0000.0000.1870.2500.1940.0310.1860.3090.2490.3051.0000.0280.213
others0.0340.0360.0420.0730.0640.0330.0840.0000.0170.1750.0240.0800.0000.1070.0260.0000.1060.0281.0000.061
furnishing_type0.1750.0220.0430.1660.1920.0260.1320.0910.0000.2380.0850.1770.0540.2140.1360.2650.1540.2130.0611.000

Missing values

2023-12-05T21:16:00.628420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T21:16:00.916230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-05T21:16:01.117147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatshree vardhman victoriasector 701.557948.01950.0Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)3336.0South-EastRelatively New1950.0NaN1161.011011049
1flatsupertech aravillesector 790.716061.01171.0Super Built up area 1295(120.31 sq.m.)Carpet area: 1250 sq.ft. (116.13 sq.m.)223+17.0NorthNew Property1295.0NaN1250.000001053
2flatss the leafsector 851.2213318.0916.0Super Built up area 1772(164.62 sq.m.)Built Up area: 1300 sq.ft. (120.77 sq.m.)Carpet area: 916 sq.ft. (85.1 sq.m.)22316.0North-EastRelatively New1772.01300.0916.000100081
3flatdlf new town heightssector 901.258638.01447.0Carpet area: 1447 (134.43 sq.m.)343+0.0North-WestOld PropertyNaNNaN1447.001000231
4flatchd avenuesector 710.957929.01198.0Super Built up area 1198(111.3 sq.m.)2225.0SouthModerately Old1198.0NaNNaN000001142
5flatemaar mgf the palm drivesector 663.2017777.01800.0Super Built up area 2200(204.39 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)33312.0South-WestRelatively New2200.0NaN1800.0111100117
6flatm3m heightssector 652.2915980.01433.0Carpet area: 1433 (133.13 sq.m.)2238.0NaNUnder ConstructionNaNNaN1433.010000015
7flatcentral park flower valley aqua front towerssector 332.1011738.01789.0Super Built up area 1789(166.2 sq.m.)3326.0NaNUnder Construction1789.0NaNNaN00000024
8flatorchid petalssector 492.4511887.02061.0Super Built up area 2061(191.47 sq.m.)3330.0North-EastRelatively New2061.0NaNNaN10000049
9housedlf alamedasector 7317.0035109.04842.0Plot area 4842(449.84 sq.m.)Built Up area: 9000 sq.ft. (836.13 sq.m.)5833.0North-WestRelatively NewNaN9000.0NaN11110287
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3775flattulip violetsector 691.208888.01350.0Super Built up area 1350(125.42 sq.m.)2310.0North-WestRelatively New1350.0NaNNaN000000150
3776houseindependentsector 485.5023504.02340.0Plot area 260(217.39 sq.m.)4422.0NorthRelatively NewNaN2340.0NaN01000122
3777flatbreez global hill viewsohna road2.6046917.0554.0Carpet area: 554.16 (51.48 sq.m.)2226.0NaNNew PropertyNaNNaN554.12557200000042
3778flatchanderlok societysector 280.907234.01244.0Carpet area: 1244 (115.57 sq.m.)423+1.0NaNOld PropertyNaNNaN1244.0000000000000
3779flatbestech park view residencysector 20.558461.0650.0Super Built up area 650(60.39 sq.m.)1127.0NorthModerately Old650.0NaNNaN00000031
3780flatpareena mi casasector 681.159236.01245.0Super Built up area 1245(115.66 sq.m.)2238.0EastNew Property1245.0NaNNaN00000049
3781flathero homesdwarka expressway1.607825.02045.0Super Built up area 1689(156.91 sq.m.)3331.0NaNNew Property1689.0NaNNaN00000049
3782flatemaar mgf emerald floors premiersector 653.5017721.01975.0Super Built up area 1975(183.48 sq.m.)4434.0EastRelatively New1975.0NaNNaN010001114
3783flatparsvnath green villesector 481.508787.01707.0Super Built up area 1707(158.59 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)3331.0NorthOld Property1707.01600.01400.000000000001128
3784flatsmriti apartmentsector 560.6010000.0600.0Super Built up area 600(55.74 sq.m.)1112.0NaNModerately Old600.0NaNNaN00000049